Time series analysis based on ordinal pattern statistics
Previous work by Rosso et al. [1] showed that time series from low dimensional chaotic systems can be distinguished from stochastic processes (“noise”) by their location in the complexity-entropy plane spanned by the normalized entropy \( H_S [P] = S[P] / S_{max} \) given by the Shannon entropy \( S[P] = -\sum_{j=1}^{m!} p_j \log p_j \) divided by its maximal value \( S_{max} = 1 / \log(m!